Merge pull request #219846 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: rmca14

articles/azure-app-configuration/rest-api.md

@@ -5,14 +5,12 @@ author: maud-lv
 ms.author: malev
 ms.service: azure-app-configuration
 ms.topic: reference
-ms.date: 08/17/2020
+ms.date: 11/28/2022
 ---
 
-# Azure App Configuration REST API
+# Azure App Configuration Data Plane REST API
 
-High-level documentation on the REST API for Azure App Configuration is available in the main [Azure REST documentation](/rest/api/appconfiguration/). This section contains more details for developers working with the App Configuration REST API.
-
-The following reference pages are available to describe the Azure App Configuration API surface in detail.
+The documentation on the [control plane](/azure/azure-resource-manager/management/control-plane-and-data-plane#control-plane) REST API for Azure App Configuration is available in the [Azure REST documentation](/rest/api/appconfiguration/). The following reference pages describe the [data plane](/azure/azure-resource-manager/management/control-plane-and-data-plane#data-plane) REST API for Azure App Configuration. The data plane REST API is available at the endpoint of an App Configuration store, for example, `https://{store-name}.azconfig.io`.
 
 ## Resources
 
@@ -34,4 +32,4 @@ The following reference pages are available to describe the Azure App Configurat
 ## Development
 
 - [Fiddler](./rest-api-fiddler.md)
-- [Postman](./rest-api-postman.md)
+- [Postman](./rest-api-postman.md)

articles/azure-web-pubsub/reference-functions-bindings.md

@@ -85,7 +85,7 @@ public static void Run(
     string data,
     WebPubSubDataType dataType)
 {
-    Console.WriteLine($"Request from: {context.userId}");
+    Console.WriteLine($"Request from: {context.UserId}");
     Console.WriteLine($"Request message data: {data}");
     Console.WriteLine($"Request message dataType: {dataType}");
 }

articles/deployment-environments/quickstart-create-access-environments.md

@@ -97,7 +97,7 @@ Complete the following steps in the Azure CLI to create an environment and confi
 
    ```azurecli
    az devcenter dev environment create --dev-center-name <devcenter-name> 
-       --project-name <project-name> -n <name> --environment-type <environment-type-name> 
+       --project-name <project-name> --environment-type <environment-type-name> 
        --catalog-item-name <catalog-item-name> --catalog-name <catalog-name> 
    ```
 

articles/kubernetes-fleet/architectural-overview.md

@@ -27,7 +27,7 @@ Fleet supports joining the following types of existing AKS clusters as member cl
 
 During preview, you can join up to 20 AKS clusters as member clusters to the same fleet resource.
 
-Once a cluster is joined to a fleet resource, a MemeberCluster custom resource is created on the fleet.
+Once a cluster is joined to a fleet resource, a MemberCluster custom resource is created on the fleet.
 
 The member clusters can be viewed by running the following command:
 

articles/machine-learning/how-to-create-data-assets.md

@@ -67,7 +67,7 @@ When you create a data asset in Azure Machine Learning, you'll need to specify a
 ## Data asset types
  - [**URIs**](#Create a `uri_folder` data asset) - A **U**niform **R**esource **I**dentifier that is a reference to a storage location on your local computer or in the cloud that makes it easy to access data in your jobs. Azure Machine Learning distinguishes two types of URIs:`uri_file` and `uri_folder`.
 
- - [**MLTable**](#Create a `mltable` data asset) - `MLTable` helps you to abstract the schema definition for tabular data so it is more suitable for complex/changing schema or to be used in AutoML. If you just want to create a data asset for a job or you want to write your own parsing logic in python you could use `uri_file`, `uri_folder`.
+ - [**MLTable**](#Create a `mltable` data asset) - `MLTable` helps you to abstract the schema definition for tabular data so it is more suitable for complex/changing schema or to be used in AutoML. If you just want to create a data asset for a job or you want to write your own parsing logic in Python you could use `uri_file`, `uri_folder`.
 
  The ideal scenarios to use `mltable` are:
  - The schema of your data is complex and/or changes frequently.

articles/purview/concept-best-practices-network.md

@@ -85,7 +85,7 @@ Here are some best practices:
 
 When you're scanning a data source in Microsoft Purview, you need to provide a credential. Microsoft Purview can then read the metadata of the assets by using the Azure integration runtime in the destination data source. When you're using a public network, authentication options and requirements vary based on the following factors: 
 
-- **Data source type**. For example, if the data source is Azure SQL Database, you need to use SQL authentication with db_datareader access to each database. This can be a user-managed identity or a Microsoft Purview managed identity. Or it can be a service principal in Azure Active Directory added to SQL Database as db_datareader. 
+- **Data source type**. For example, if the data source is Azure SQL Database, you need to use a login with db_datareader access to each database. This can be a user-managed identity or a Microsoft Purview managed identity. Or it can be a service principal in Azure Active Directory added to SQL Database as db_datareader. 
 
   If the data source is Azure Blob Storage, you can use a Microsoft Purview managed identity, or a service principal in Azure Active Directory added as a Blob Storage Data Reader role on the Azure storage account. Or use the storage account's key.  
 

articles/spring-apps/how-to-dump-jvm-options.md

@@ -53,7 +53,7 @@ Use the following `--jvm-options` parameter to generate GC logs. For more inform
 Use the following `--jvm-options` parameter to generate a JFR file.  For more information, see the official JVM documentation.
 
 ```azurecli
---jvm-options="-XX:StartFlightRecording=dumponexit=true,dumponexitpath=<path-to-JFR-file>"
+--jvm-options="-XX:StartFlightRecording=dumponexit=true,filename=<path-to-JFR-file>"
 ```
 
 ## Configure the path for generated files